The DOI matches its Crossref record.
Machine learning approach for predicting bumps on road
Abstract
In today's days, due to increase in number of vehicles the probability of accidents are also increasing. The user should be aware of the road circumstances for safety purpose. Several methods requires installing dedicated hardware in vehicle which are expensive. so we have designed a Smart-phone based method which uses a Accelerometer and GPS sensors to analyze the road conditions. The designed system is called as Bumps Detection System(BDS) which uses Accelerometer for pothole detection and GPS for plotting the location of potholes on Google Map. Drivers will be informed in advance about count of potholes on road. we have assumed some threshold values on z-axis(Experimentally Derived)while designing the system. To justify these threshold values we have used a machine learning approach. The k means clustering algorithm is applied on the training data to build a model. Random forest classifier is used to evaluate this model on the test data for better prediction.
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[34] uses a similar approach to detect road anomalies, clustering the data with the k-Means method.
“A similar approach to detect road anomalies is used in [34], and the data in this research are also clustered using the k-Means method.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [34]
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No references could be resolved yet.
Research works printed but not linked: 9
- mednis, 2011, Real time pothole detection using Android smartphones with accelerometerss, iProc International Conference on Distributed Computing in Sensor Systems and Workshops, 1
- chen, 2011, Road Condition Monitoring Using On-board Three-axis Accelerometer and GPS Sensor, sixth International ICST Conference on Communications and Networking
- kulkarni, 2014, Pothole Detection System using Machine Learning on Android, International Journal of Emerging Technology and Advanced Engineering, 4
- fazeen, 2012, Safe Driving Using Mobile Phonet, Proceedings of IEEE Transaction On Intelligent Transportation system, 13
- goregaonkar, 2014, Assistance to Driver and Monitoring the Accidents on Road by using Three Axis Accelerometer and GPS System, Proc 1st Int Conf, 10, 1
- 10.4108/ICST.PERVASIVEHEALTH2010.8901 · DOI
- 10.1145/1460412.1460444 · DOI
- 10.1109/TVT.2009.2027710 · DOI
- girod, 2008, The pothole patrol: Using a mobile sensor network for road surface monitoring, Sixth Annual International conference on Mobile Systems Applications and Service IEEE
Cited by
Sorted by citations
A Review of Machine Learning and IoT in Smart Transportation
Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019) · Future Internet · doi:10.3390/fi11040094 · Cited by 625
Metadata
- Authors
- Ghadge, M., Pandey, D., & Kalbande, D.
- ORCID
- Kalbande, Dhananjay
- Venue
- 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)
- Publisher
- IEEE
- Published
- 2015-10
- Pages
- 481-485
- OpenAlex
- W2342501680
- Cited by
- 46
- References
- 0
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 8 · 2023: 3 · 2022: 7 · 2021: 9 · 2020: 4 · 2019: 5 · 2017: 1
- FWCI
- 1.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Applied and Theoretical Computing and Communication Technology (publishedVersion)
- Funders
- Samsung
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Automotive Engineering › IoT and GPS-based Vehicle Safety Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- pothole detection, smartphone sensing, accelerometer, GPS, road condition monitoring, driver safety, K-means clustering, random forest classification, cut-off values
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